Claims checked against evidence, page 3 of 15
Claims by Gary Marcus and Ed Zitron. Deadlines are judged as of 2026-10-29.
Claims checked against evidence (1432)
Each of these claims was checked against dated evidence.
Page 3 of 15: claims 201 to 300 of 1432, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: SupportedSaid
Gary Marcus Observation
“the fact that LLMs hallucinate (patently true)”
Our reading: LLMs hallucinate.
No horizon Evidence as of
Limitation: The evidence is a manufacturer's internal evaluation.
Assessment: SupportedSaid
Gary Marcus Observation
“It is a misapprehension of LLMs to confuse the things they say with what is true.”
Our reading: What LLMs say should not be taken as true just because they say it; treating their statements as truth misunderstands them.
No horizon Evidence as of
Limitation: The benchmark uses one person's biography and that person's manual judgments; it cannot establish a general error rate.
Assessment: SupportedSaid
Gary Marcus Observation
“Essentially the same argument applies to Claude’s constitution, which amounts to a set of built-in guardrails. OpenAI has those, too!”
Our reading: Claude's constitution is basically a set of built-in guardrails, and OpenAI's models have guardrails too.
No horizon Evidence as of
Limitation: The outcome depends on reading guardrails broadly and amounts to as shorthand.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Apocalyptic talk is a special case of hype, Sam and Dario both play that card over and over, basically amounting to saying “our tech is so scary it might ruin society, so give us more money so we can make that tech ever faster.””
Our reading: Altman and Amodei both repeatedly use apocalyptic talk, a special case of hype, which basically amounts to saying their tech is so scary it might ruin society so they should be given more money to build it ever faster.
No horizon Evidence as of
Limitation: Hype is an opinion rather than a separately measurable fact.
Assessment: SupportedSaid
Gary Marcus Observation
“How about copyright? OpenAI notoriously trained on practically anything that is “publicly available” whether public domain or not.”
Our reading: OpenAI trained on practically anything publicly available, whether public domain or not.
No horizon Evidence as of
Limitation: Support applies to broad use including copyrighted material, not a numerical interpretation of “practically anything.”
Assessment: UnresolvedSaid
Gary Marcus Forecast
“AI can help in the search for candidate drugs, but we still need to test them. There is zero chance that we double human life span in the next decade.”
Our reading: There is zero chance that human life span is doubled in the next decade.
Not yet due Evidence as of
Limitation: The lifespan measure, population and March 2026 baseline remain unspecified.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Both, for example, have endlessly hinted that AGI is near, and just quietly moved the deadline back when they didn’t deliver.”
Our reading: Amodei and Altman have repeatedly hinted AGI is near and quietly moved the deadline back when it didn't arrive.
No horizon Evidence as of
Limitation: Evidence for individual components does not settle the compound claim.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Millions of people, literally, are angry; many feel betrayed.”
Our reading: Millions of people are angry and many feel betrayed by Altman/OpenAI.
No horizon Evidence as of
Limitation: Anger and betrayal are not measured directly. Boycott sign-ups and uninstalls are proxies that may reflect other motives.
Source: BREAKING: Sam Altman’s greed and dishonesty are finally catching up to him
Assessment: SupportedSaid
Gary Marcus Observation
“The public anger on social media is growing fast. A major boycott is afoot:”
Our reading: Public anger at OpenAI on social media is growing fast and a major boycott is underway.
No horizon Evidence as of
Limitation: No platform-wide time series establishes the precise growth rate of angry social-media activity.
Source: BREAKING: Sam Altman’s greed and dishonesty are finally catching up to him
Assessment: SupportedSaid
Gary Marcus Observation
“Using a system you shouldn’t trust with your taxes to blow people up. 2026 in a nutshell.”
Our reading: Generative AI, a system that people shouldn't trust with their taxes, is being used by certain parts of Washington to blow people up.
No horizon Evidence as of
Limitation: The result depends on reading the military phrase as operational support rather than autonomous firing.
Source: Don’t trust Generative AI to do your taxes — and don’t trust it with people’s lives
Assessment: UnresolvedSaid
Gary Marcus Observation
“Essentially anyone who uses a chatbot is at risk.”
Our reading: Essentially anyone who uses a chatbot is at risk of having their beliefs distorted by sycophantic AI.
No horizon Evidence as of
Limitation: Treating essentially anyone as broad population coverage materially affects the assessment; a weaker claim that ordinary users can be affected is better supported.
Source: Breaking: “sycophantic AI distorts belief, manufacturing certainty where there should be doubt”
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“It has made dodgy companies that gave lip service to AI safety tremendously wealthy —on the promise, not yet delivered—that they could create AGI.”
Our reading: Doomer hype has made dodgy companies that gave lip service to AI safety tremendously wealthy on the not-yet-delivered promise that they could create AGI.
No horizon Evidence as of
Limitation: Financing and valuation are distinct measures and are used only as context.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“By proclaiming that LLMs would change the world, and that they would do so imminently, doomers contributed to the growth of the LLM companies (essentially free publicity) and to the White House thinking LLMs were critical to the survival of the nation.”
Our reading: By proclaiming that LLMs would change the world imminently, doomers contributed to the growth of LLM companies and to the White House thinking LLMs were critical to the nation's survival.
No horizon Evidence as of
Limitation: Public explanations may omit influences present in private deliberations.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“the point being that doomers, especially those who have screamed that AGI is nigh, have wanted to slow AI acceleration (at least until we could make AI safe) and instead have only accelerated it.”
Our reading: Doomers, especially those who claimed AGI is nigh, wanted to slow AI acceleration (at least until AI could be made safe) but instead have only accelerated it.
No horizon Evidence as of
Limitation: Safety agreements are not measurements of development speed.
Assessment: SupportedSaid
Gary Marcus Observation
“And of course Generative AI still has problems with common sense, as an endless of stream of examples have shown, this one involving circulating on X today:”
Our reading: Generative AI still has problems with common sense.
No horizon Evidence as of
Limitation: The main systematic test comes from a commercial vendor.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Worse, they did it in favor of someone else who took pretty similar terms and happened to have given more campaign contributions.”
Our reading: The government acted in favor of someone else (the OpenAI/Altman side) who took pretty similar terms to Anthropic's and happened to have given more campaign contributions.
No horizon Evidence as of
Limitation: Executives' personal donations are not corporate contributions.
Source: The whole thing was a scam
Assessment: SupportedSaid
Gary Marcus Observation
“It’s one thing for the government to reject Anthropic’s terms—and entirely another to banish them permanently and, absurdly and punitively declare them a supply chain risk.”
Our reading: The US government permanently banished Anthropic and absurdly and punitively declared it a supply chain risk, going beyond merely rejecting its terms.
No horizon Evidence as of
Limitation: Formal designation followed in March.
Source: The whole thing was a scam
Assessment: SupportedSaid
Gary Marcus Observation
“The company ripped off a lot of writer’s work (per the $1.5B settlement), and recently walked back its core safety pledge.”
Our reading: Anthropic ripped off a lot of writers' work, as shown by its $1.5B settlement.
No horizon Evidence as of
Limitation: The assessment reads 'ripped off' as unauthorized acquisition, not unlawful training.
Source: The whole thing was a scam
Assessment: SupportedSaid
Gary Marcus Observation
“Anthropic refused to back down.”
Our reading: Anthropic refused to back down in its dispute with the US government over military use of its AI.
No horizon Evidence as of
Limitation: This assesses the company's position during the stated period, not the eventual outcome of negotiations.
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“If anything really bad comes of pushing premature AI too hard and too fast into to the military, he will own it.”
Our reading: If pushing premature AI too hard and too fast into the military leads to something really bad, Trump will be held responsible.
No deadline stated Evidence as of
Only if: if something really bad comes of pushing premature AI too hard and too fast into the military
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“One thing that Noble doesn’t mention is why OpenAI has struggled to make a profit; in my eyes, there are three reasons, that I have emphasized here before: the product is unreliable (placing an upper bound on how much many people be willing to pay), it is expensive to operate, and they have no technical moat, which has led to price wars.”
Our reading: In Marcus's view, one reason OpenAI has struggled to make a profit is that its product is unreliable, which places an upper bound on how much many people are willing to pay for it.
No horizon Evidence as of
Limitation: General AI survey findings do not directly measure OpenAI purchasing behavior.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“But that is exactly where we are headed.”
Our reading: We are headed exactly toward a situation where one individual, a Cabinet official, decides at gunpoint on mass surveillance and AI-fueled weapons, possibly nuclear, without humans in the loop.
No deadline stated Evidence as of
Limitation: Public records cannot establish the contents of undisclosed contracts or classified implementation.
Source: America, and probably the world, stands on a precipice.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Hegsseth’s demand would in principle extend to apply Anthropic’s software to nuclear weapons without humans in the loop.”
Our reading: Hegseth's demand would in principle allow Anthropic's software to be applied to nuclear weapons without humans in the loop.
No horizon Evidence as of
Limitation: Rests on interpretation of 'lawful' and 'in principle'.
Source: America, and probably the world, stands on a precipice.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Generative AI is NOT remotely reliable enough to make life or death decisions of magnitude.”
Our reading: Generative AI is nowhere near reliable enough to make major life-or-death decisions.
No horizon Evidence as of
Limitation: The claim leaves both the application and acceptable risk unspecified.
Source: 30 seconds to midnight? 15?
Assessment: SupportedSaid
Gary Marcus Observation
“Although there are some practical use cases, such as coding, it is an inherently unreliable technology.”
Our reading: Generative AI has some practical uses such as coding but is an inherently unreliable technology.
No horizon Evidence as of
Limitation: Coding utility is supported here by self-reports; the experiment does not establish gains across coding tasks [e1, e3].
Assessment: SupportedSaid
Gary Marcus Causal claim
“It is leading some people into serious mental health issues.”
Our reading: Generative AI is leading some people into serious mental health issues.
No horizon Evidence as of
Limitation: One clinical case does not establish population prevalence or quantify causal risk [e1].
Assessment: UnresolvedSaid
Gary Marcus Observation
“Turns out it wasn’t, as Shira Ovide just reported:”
Our reading: Generative AI was not in fact contributing half of US GDP growth, contrary to the White House AI advisor's claim.
No horizon Evidence as of
Limitation: This does not validate the advisor's half-of-growth assertion.
Assessment: UnresolvedSaid
Gary Marcus Observation
“I don’t know that Generative AI was literally a scam, but the people selling it have tried to sell it as if it were tantamount to artificial general intelligence, when it’s not.”
Our reading: Generative AI vendors have marketed it as tantamount to AGI, but it is not AGI.
No horizon Evidence as of
Limitation: Altman's quotation comes from a third-party transcript and does not establish what all vendors promised [e1].
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Generative AI has its uses, but it hardly surprising that most business have struggled to find return on their investment, given the massive reliability issues that continue to plaguage it.”
Our reading: Most businesses have struggled to get return on generative AI investment because of its ongoing massive reliability issues.
No horizon Evidence as of
Limitation: NANDA's interviews, conference survey and public-initiative review do not establish prevalence among all generative-AI investors [e1].
Assessment: SupportedSaid
Gary Marcus Observation
“They often produce workslop.”
Our reading: Large language models often produce low-quality 'workslop'.
No horizon Evidence as of
Limitation: Self-reported experience is not an independently graded output sample.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Across domains involving causality, value trade-offs, justification, and error monitoring, humans attend to reasons, stakes, and responsibility, whereas language models optimize for linguistic plausibility—structural fault lines between performance alignment and genuine evaluative competence (Quattrociocchi et al., 2025).”
Our reading: Across domains involving causality, value trade-offs, justification, and error monitoring, humans attend to reasons, stakes, and responsibility, whereas language models optimize for linguistic plausibility.
No horizon Evidence as of
Limitation: A model can be trained for correctness while still making unreliable judgments.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: UnresolvedSaid
Gary Marcus Observation
“Behavioral similarity alone therefore provides no insight into whether the underlying processes support core components of general intelligence, such as judgment, error correction, or reliable generalization.”
Our reading: LLMs' behavioral similarity to humans alone gives no insight into whether they have judgment, error correction, or reliable generalization.
No horizon Evidence as of
Limitation: The strongest relevant studies use additional tests beyond behavioral similarity.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: SupportedSaid
Gary Marcus Observation
“Moreover, benchmarks are often easily gamed.”
Our reading: AI benchmarks are often easily gamed.
No horizon Evidence as of
Limitation: 'Often' has no threshold. The evidence covers prominent benchmarks, not all benchmarks.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: SupportedSaid
Gary Marcus Observation
“Reports that language models have produced correct proofs for isolated open problems in mathematics, including specific Erdős problems, do not alter this assessment.”
Our reading: LLM proofs of isolated open math problems, including Erdős problems, do not show general intelligence.
No horizon Evidence as of
Limitation: The 'do not alter this assessment' part is an evaluative judgment.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: SupportedSaid
Gary Marcus Observation
“Current AI systems are powerful and increasingly useful tools, but they do not exhibit the flexible, self-directed competence that the original concept of artificial general intelligence was intended to capture.”
Our reading: Current AI systems are powerful and increasingly useful tools, but they do not exhibit the flexible, self-directed competence that the original concept of AGI was intended to capture.
No horizon Evidence as of
Limitation: Most of the weight rests on one benchmark, ARC-AGI-3. Its 100% mark means matching human efficiency, not only solving the games.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: UnresolvedSaid
Gary Marcus Observation
“We also show that recent claims (Chen et al., 2026) about putative success on AGI hinge on redefining what AGI has historically meant.”
Our reading: Chen et al.'s (2026) claims that AGI has been achieved depend on redefining the historical meaning of AGI.
No horizon Evidence as of
Limitation: The packet lacks the complete Chen et al. argument required by the contract.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: UnresolvedSaid
Gary Marcus Observation
“Language models, by contrast, tended to issue confident classifications in the same cases, even when uncertainty was explicitly signaled in the input.”
Our reading: In the cited direct comparison of human and LLM judgments of news source reliability, language models tended to issue confident classifications in the same cases where humans withheld or downgraded confidence, even when uncertainty was explicitly signaled in the input.
No horizon Evidence as of
Limitation: The supplementary material and usable underlying data were unavailable through the reader.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: SupportedSaid
Gary Marcus Observation
“Task-level performance, even when impressive, is not sufficient evidence of general intelligence.”
Our reading: Impressive task-level performance is not sufficient evidence of general intelligence.
No horizon Evidence as of
Limitation: The empirical example concerns particular systems and tasks, not every model available in February 2026.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: UnresolvedSaid
Gary Marcus Observation
“Such claims confuse high performance on benchmarks (often gameable) with real world flexibility, which historically has been at the center of what it means to be an artificial general intelligence.”
Our reading: Claims that AGI has already arrived confuse high performance on benchmarks, which are often gameable, with real-world flexibility, which historically has been at the center of what it means to be an AGI.
No horizon Evidence as of
Limitation: The university report provides a direct author quotation but is not the complete Nature argument.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: UnresolvedSaid
Gary Marcus Observation
“Rumors that humanity has already achieved artificial general intelligence (AGI) have been greatly exaggerated.”
Our reading: AGI has not already been achieved; rumors that it has are greatly exaggerated.
No horizon Evidence as of
Limitation: This is a historical assessment; later model improvements do not contradict the claim.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: SupportedSaid
Gary Marcus Observation
“By the standards articulated in the original definitions of artificial general intelligence—robustness across environments, reliable generalization under novelty, and autonomous goal-directed behavior—current AI systems remain limited.”
Our reading: By original AGI standards, current AI systems remain limited.
No horizon Evidence as of
Limitation: The decisive benchmark results came out about five weeks after the post.
Source: Rumors of AGI’s arrival have been greatly exaggerated
Assessment: UnresolvedSaid
Gary Marcus Observation
“Scammers will be among the first to adopt these tools. And indeed they already have;”
Our reading: Scammers are among the first adopters of AI impersonation and deepfake tools and are already using them.
No horizon Evidence as of
Limitation: Actual use does not by itself establish comparative adoption order.
Source: We URGENTLY need a federal law forbidding AI from impersonating humans
Assessment: SupportedSaid
Gary Marcus Observation
“1Some state laws are trying to address these issues, but as we know the Federal government is trying to undermine the states’ abilities to pass and enforce AI regulation.”
Our reading: The US federal government is trying to undermine states' ability to pass and enforce AI regulation.
No horizon Evidence as of
Limitation: This finding establishes an attempted restriction, not that state laws had already been invalidated or enforcement stopped.
Source: We URGENTLY need a federal law forbidding AI from impersonating humans
Assessment: SupportedSaid
Gary Marcus Observation
“Well here’s a new one, you can have OpenClaw call people, and pretend to be human.”
Our reading: OpenClaw can be used to phone people while pretending to be human, a new security risk.
No horizon Evidence as of
Limitation: Requires installation, configuration and a telephony provider.
Source: We URGENTLY need a federal law forbidding AI from impersonating humans
Assessment: UnresolvedSaid
Gary Marcus Observation
“Anybody’s appearance can (given sufficient data) be faked now, at any time, for almost nothing.”
Our reading: Given sufficient data, deepfake tools can now fake anybody's appearance at any time for almost no cost.
No horizon Evidence as of
Limitation: The scientific synthesis is broader than person-specific video impersonation.
Source: We URGENTLY need a federal law forbidding AI from impersonating humans
Assessment: UnresolvedSaid
Gary Marcus Observation
“OpenAI is losing money every quarter; even by their own (optimistic) calculations, they won’t make a profit for years.”
Our reading: OpenAI loses money every quarter and, even by its own optimistic projections, will not be profitable for years.
No horizon Evidence as of
Limitation: The packet contains no comparable series of quarterly net results through October–December 2025.
Assessment: SupportedSaid
Gary Marcus Observation
“Google and Anthropic have each largely caught up; various Chinese companies are closing in.”
Our reading: Google and Anthropic have largely caught up with OpenAI, and Chinese companies are closing in.
No horizon Evidence as of
Limitation: The result depends on interpreting the statement as a claim about model capabilities.
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“If the venture capital dries up, they are toast. Talent will make a beeline for competitors.”
Our reading: If venture capital dries up, OpenAI will collapse and its talent will flee to competitors.
No deadline stated Evidence as of
Only if: if venture capital dries up
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Or even how he eventually walked that back to 2026 (which also won’t happen).”
Our reading: Elon Musk's promise of a million Tesla robotaxis, pushed back to 2026, will not be fulfilled in 2026.
Not yet due Evidence as of
Limitation: The December 31, 2026 deadline had not passed at the October 28 evaluation date.
Source: Promises are cheap
Assessment: SupportedSaid
Gary Marcus Observation
“Back on planet earth, according to December’s Remote Labor Index, only 2.5% of human “online tasks” (physical tasks were excluded) could be done by AI.”
Our reading: According to the December 2025 Remote Labor Index, AI could do only 2.5% of human online tasks.
No horizon Evidence as of
Limitation: The judgment depends on keeping the claim within the named benchmark’s scope.
Source: Promises are cheap
Assessment: SupportedSaid
Gary Marcus Observation
“The security of the autogenerated code is very much in question, as for example this article last week noted (precisely as this substack has long warned):”
Our reading: The security of AI-generated code is seriously in doubt.
No horizon Evidence as of
Limitation: One main study comes from a security vendor with a commercial interest in highlighting risk. The other is a preprint.
Source: About that Matt Shumer post that has nearly 50 million views
Assessment: SupportedSaid
Gary Marcus Observation
“LLMs write code wicked fast, but some coders are starting to report burnout, and only relatively modest gains relative to that burnout, as a new story from Connie Loizos at Tech Crunch illustrates”
Our reading: LLMs write code very fast, but some coders report burnout and only modest gains compared with that burnout.
No horizon Evidence as of
Limitation: The evidence is a small set of reports: one interviewed engineer and one anonymous forum commenter. It does not show how common burnout is among coders.
Source: About that Matt Shumer post that has nearly 50 million views
Assessment: SupportedSaid
Gary Marcus Observation
“LLMs still hallucinate and continue to make boneheaded errors.”
Our reading: As of February 2026, LLMs still hallucinate and still make boneheaded errors.
No horizon Evidence as of
Limitation: Vectara measures faithfulness in summaries only; it does not cover other kinds of hallucination.
Source: BREAKING: LLM “reasoning” continues to be deeply flawed
Assessment: SupportedSaid
Gary Marcus Observation
“And reasoning is still one of the core issues.”
Our reading: As of February 2026, reasoning is still one of the core unsolved problems for LLMs.
No horizon Evidence as of
Limitation: 'Core issue' is qualitative; no metric ranks reasoning failures against other limitations.
Source: BREAKING: LLM “reasoning” continues to be deeply flawed
Assessment: UnresolvedSaid
Gary Marcus Observation
“Although there is some clear value in it, the costs are high, and relative to those costs, the revenue has been modest.”
Our reading: Generative AI has some clear value, but its costs are high and its revenue has been modest relative to those costs.
No horizon Evidence as of
Limitation: The financial reporting concerns one provider.
Source: Some disconcerting facts about AI and banking that may have profound consequences
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Let me add that I do think that some form of AI will eventually actually yield the returns people were anticipating for Generative AI.”
Our reading: Some form of AI will eventually yield the returns people anticipated from generative AI.
No deadline stated Evidence as of
Limitation: Goldman Sachs provides an illustrative forecast, not realized gains or a threshold Marcus explicitly adopted.
Source: Some disconcerting facts about AI and banking that may have profound consequences
Assessment: SupportedSaid
Gary Marcus Observation
“Not an all out crash, but an unmistakable sign that its ascent has run out of gas.”
Our reading: Nvidia's flat stock since August 2025 shows its rise has run out of gas, though it has not crashed.
No horizon Evidence as of
Limitation: Prices are intraday figures from Barron's, not official closes.
Source: Sam Altman and the day Nvidia’s meteoric rise came to an end
Assessment: SupportedSaid
Gary Marcus Observation
“They have also faced a lot of backlash for being reckless and irresponsible; hardly a good brand name.”
Our reading: xAI has faced a lot of backlash for being reckless and irresponsible, which is hardly a good brand name.
No horizon Evidence as of
Limitation: The brand comment is assessed qualitatively.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Me, I think the merger is really a kind of bailout, to give a lot of cash to a company that is otherwise in distress.”
Our reading: Marcus thinks the SpaceX-xAI merger is really a kind of bailout, meant to give a lot of cash to xAI, which is otherwise in distress.
No horizon Evidence as of
Limitation: The fundraising announcement does not disclose unrestricted liquidity after outstanding commitments [e2].
Assessment: SupportedSaid
Gary Marcus Observation
“OpenClaw itself is basically a cascade of LLMs.”
Our reading: OpenClaw is basically a cascade of LLMs.
No horizon Evidence as of
Limitation: Cascade is informal terminology.
Source: OpenClaw (a.k.a. Moltbot) is everywhere all at once, and a disaster waiting to happen
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“The point is that when it costs pennies to coordinate a fake mob and moments to counterfeit a human identity, the public square is left wide open to attack.”
Our reading: When it costs pennies to coordinate a fake mob and takes moments to counterfeit a human identity, the public square is left wide open to attack.
No horizon Evidence as of
Only if: it costs pennies to coordinate a fake mob
Assessment: SupportedSaid
Gary Marcus Observation
“Today’s swarms, now emerging, can coordinate fleets of synthetic personas—sometimes with persistent identities—and move in ways that are hard to distinguish from real communities.”
Our reading: Emerging AI bot swarms can coordinate fleets of synthetic personas that are hard to distinguish from real communities.
No horizon Evidence as of
Limitation: 'Hard to distinguish' has no measure. The evidence does not show how long platforms or ordinary users took to detect these networks.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Even so-called “thinking” AI models are vulnerable to this,”
Our reading: Even so-called 'thinking' (reasoning) AI models are vulnerable to training-data poisoning by swarms.
No horizon Evidence as of
Limitation: The evidence includes no audit that names reasoning models and measures how often they repeat narratives seeded by grooming.
Assessment: SupportedSaid
Gary Marcus Observation
“Venture capital is already helping industrialize astroturfing: Doublespeed, backed by Andreessen Horowitz, advertises a way to “orchestrate actions on thousands of social accounts” and to mimic “natural user interaction” on physical devices so the activity appears human.”
Our reading: Venture capital is already helping industrialize astroturfing; for example, Andreessen Horowitz-backed Doublespeed advertises a way to orchestrate actions on thousands of social accounts and to mimic natural user interaction on physical devices so the activity appears human.
No horizon Evidence as of
Limitation: Historical advertising wording rests on accessible reporting because the primary website has changed and the original 404 Media report was only partially accessible.
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“If we require this kind of “proof-of-human” for high-reach interactions, we make it mathematically difficult and financially ruinous for one operator to secretly run ten thousand accounts.”
Our reading: If verified-yet-anonymous proof-of-human is required for high-reach interactions, it will make it mathematically difficult and financially ruinous for one operator to secretly run ten thousand accounts.
No deadline stated Evidence as of
Only if: this kind of proof-of-human is required for high-reach interactions
Assessment: SupportedSaid
Gary Marcus Observation
“And it’s just getting worse.”
Our reading: The problem of chatbot-associated deaths is getting worse.
No horizon Evidence as of
Limitation: The assessment counts reported cases and lawsuits. It does not use measured death rates, and the evidence includes no per-user rate.
Assessment: SupportedSaid
Gary Marcus Observation
“And then there is the growing problem of chatbots apparently inducing delusions in humans, that Kashmir Hill and others have been documenting.”
Our reading: Chatbots apparently inducing delusions in humans is a growing problem.
No horizon Evidence as of
Limitation: The evidence covers documented cases, not true incidence. Awareness may inflate the trend.
Assessment: SupportedSaid
Gary Marcus Observation
“By now, there have been several reports of chatbot-associated deaths (both suicide and murder), enough that wikipedia has started keeping track.”
Our reading: By now there have been several reports of chatbot-associated deaths, both suicides and murders, enough that Wikipedia has started keeping track of them.
No horizon Evidence as of
Limitation: The reported associations do not establish that chatbots caused the deaths; the hearing account and complaint record allegations [e2, e3].
Assessment: SupportedSaid
Gary Marcus Observation
“That deal was OpenAI’s to lose. And they lost it.”
Our reading: OpenAI lost the Apple Siri generative AI deal to Google.
No horizon Evidence as of
Limitation: Support depends on lost meaning did not secure the opportunity.
Assessment: ContradictedSaid
Gary Marcus Observation
“LLMs are commodities; DeepSeek proved that.”
Our reading: LLMs are commodities, as DeepSeek demonstrated.
No horizon Evidence as of
Limitation: The demand study covers one marketplace through April 2025, not the whole market through January 13, 2026 [e2].
Assessment: UnresolvedSaid
Gary Marcus Observation
“OpenAI has never made a profit.”
Our reading: OpenAI has never made a profit.
No horizon Evidence as of
Limitation: The financial evidence is news reporting about statements, not directly inspected consolidated statements.
Assessment: SupportedSaid
Gary Marcus Observation
“Many, many people were disappointed with GPT-5, arguably among the most overhyped products in history.”
Our reading: Many, many people were disappointed with GPT-5, which was arguably among the most overhyped products in history.
No horizon Evidence as of
Limitation: Support applies to substantial disappointment, not the qualified historical ranking.
Assessment: SupportedSaid
Gary Marcus Observation
“I warned that LLMs might plateau within a year or two, and they have, that OpenAI would be mired in a lot of litigation, and they are.”
Our reading: OpenAI is mired in a lot of litigation.
No horizon Evidence as of
Limitation: The evidence covers one consolidated litigation group, not OpenAI’s complete litigation portfolio.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Anthropic has started to eat a lot of OpenAI’s lunch, taking a lot of corporate customers away.”
Our reading: Anthropic is taking many corporate customers away from OpenAI.
No horizon Evidence as of
Limitation: Menlo has an investment in Anthropic.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Over the last couple years, Altman’s public credibility has steadily waned (see e.g., Karen Hao’s bestseller, and my earlier essay on Altman in The Guardian).”
Our reading: Altman's public credibility has steadily declined over the last couple of years.
No horizon Evidence as of
Limitation: Historical values in an interactive tracker were not available in the page-reader output.
Assessment: SupportedSaid
Gary Marcus Observation
“Most importantly, other companies like Anthropic and Google caught up; xAI and various Chinese companies aren’t far behind.”
Our reading: Anthropic and Google have caught up with OpenAI, and xAI and Chinese companies are not far behind.
No horizon Evidence as of
Limitation: The result depends on the ordinary competitive-tier reading of caught up and not far behind.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Those price wars did in fact come to pass; users costs per token are roughly a hundred times lower two year later.”
Our reading: LLM price wars occurred and user costs per token are roughly 100 times lower than two years earlier.
No horizon Evidence as of
Limitation: A supported component is not enough to support the full conjunction.
Assessment: SupportedSaid
Gary Marcus Observation
“I also warned that LLMs would continue to have a truthiness problem and they have.”
Our reading: LLMs have continued to have a truthfulness problem.
No horizon Evidence as of
Limitation: This establishes persistence, not a uniform error rate across models or tasks.
Assessment: UnresolvedSaid
Gary Marcus Observation
“(Altman got famous hinting at the supposed miracles of GPT-5; by any reasonable measure it was both late and short of expectations.)”
Our reading: GPT-5 was both late and short of expectations.
No horizon Evidence as of
Limitation: The reported postponement concerns an approximate public timeline, not a precise missed deadline [e1].
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“DeepSeek launched a price war that forced Altman to cut prices dramatically.”
Our reading: DeepSeek's price war forced OpenAI to cut prices dramatically.
No horizon Evidence as of
Limitation: A company's public efficiency explanation may omit commercial considerations.
Assessment: SupportedSaid
Gary Marcus Observation
“Update: This is consistent with the finding of the Remote Labor Index that AI could only do about 2.5% of jobs, reported recently by the Washington Post.”
Our reading: Per the Remote Labor Index, AI can do only about 2.5% of jobs, consistent with LLMs adding little quantifiable value.
No horizon Evidence as of
Limitation: The benchmark result does not establish the share of all occupations AI can perform or aggregate economic value from LLM use [e1].
Source: Let’s be honest, Generative AI isn’t going all that well
Assessment: UnresolvedSaid
Gary Marcus Observation
“They still aren’t adding a lot of quantifiable value to the world:”
Our reading: LLMs still are not adding much quantifiable value to the world.
No horizon Evidence as of
Limitation: The claim lacks a threshold and an agreed measure of value.
Source: Let’s be honest, Generative AI isn’t going all that well
Assessment: SupportedSaid
Gary Marcus Observation
“LLMs can still cannot be trusted:”
Our reading: LLMs still cannot be trusted.
No horizon Evidence as of
Limitation: The supplied AA-Omniscience abstract does not specify whether browsing was enabled [e1].
Source: Let’s be honest, Generative AI isn’t going all that well
Assessment: UnresolvedSaid
Gary Marcus Observation
“A large fraction of what LLMs do is mostly just memorization (and Hinton was on the wrong side of this argument):”
Our reading: A large fraction of what LLMs do is mostly memorization, and Hinton was wrong on this point.
No horizon Evidence as of
Limitation: Large fraction has no stated numerical threshold.
Source: Let’s be honest, Generative AI isn’t going all that well
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“If LeCun pulls the same stuff in his new company, morale will be deathly low.”
Our reading: If LeCun keeps failing to credit others' work at his new company, morale there will be very low.
No deadline stated Evidence as of
Only if: if LeCun fails to acknowledge others' work in his new company
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“My own view, which I stand by, is that we need “neurosymbolic” contributions from both traditions if we are to get to trustworthy AI.”
Our reading: In Marcus's stated view, which he stands by, getting to trustworthy AI requires neurosymbolic contributions from both the neural network and classical symbolic traditions.
No horizon Evidence as of
Limitation: The evidence provides no agreed application scope, failure tolerance, or boundary for what counts as a contribution from either tradition.
Assessment: UnresolvedSaid
Ed Zitron Observation
“which also includes evidence that OpenAI's revenues were at around $4.5 billion by the end of September, a vast difference from the $4.3 billion for the first half of the year published by other outlets.”
Our reading: OpenAI's revenues were around $4.5 billion by the end of September 2025, a vast difference from the $4.3 billion for the first half of the year published by other outlets.
No horizon Evidence as of
Limitation: News reporting was used because matching primary financial records were not obtained.
Source: 2025, A Retrospective
Assessment: MixedSaid
Ed Zitron Causal claim
“Deep Impact, my analysis of the DeepSeek situation and why it scared the American AI industry (clue: it's cost-related and nothing to do with "national security").”
Our reading: DeepSeek scared the American AI industry because of cost, not because of national security.
No horizon Evidence as of
Limitation: The market report does not establish individual investors' or executives' private motives [e1].
Source: 2025, A Retrospective
Assessment: UnresolvedSaid
Ed Zitron Observation
“I published proof that OpenAI couldn't afford to pay Oracle and Oracle didn't have the capacity to service their farcical $300 billion, 5-year-long deal.”
Our reading: As of September 12, 2025, OpenAI couldn't afford to pay Oracle and Oracle didn't have the capacity to service their $300 billion, five-year deal.
No horizon Evidence as of
Limitation: The affordability and capacity readings materially affect the test.
Source: 2025, A Retrospective
Assessment: UnresolvedSaid
Ed Zitron Observation
“on how GPT-5's router mode actually costs OpenAI more money to run.”
Our reading: GPT-5's router mode costs OpenAI more money to run.
No horizon Evidence as of
Limitation: The comparison baseline is an evaluator interpretation.
Source: 2025, A Retrospective
Assessment: ContradictedSaid
Ed Zitron Observation
“how data center developers have no exit route, and US economic growth has become shouldered entirely by big tech.”
Our reading: Data center developers have no exit route, and US economic growth is carried entirely by big tech.
No horizon Evidence as of
Limitation: The period and literal reading materially affect this assessment.
Source: 2025, A Retrospective
Assessment: UnresolvedSaid
Ed Zitron Observation
“how its future relies on endless debt, how millions of GPUs are sitting waiting to be installed, and why it no longer makes sense to buy more GPUs.”
Our reading: NVIDIA's future relies on endless debt, millions of GPUs are sitting waiting to be installed, and it no longer makes sense to buy more GPUs.
No horizon Evidence as of
Limitation: The financial component has an unknown accounting basis.
Source: 2025, A Retrospective
Assessment: UnresolvedSaid
Ed Zitron Forecast
“how AI revenues will never pay back the cost of these GPUs”
Our reading: AI revenues will never pay back the cost of these GPUs, meaning the NVIDIA GPUs being bought for AI.
No deadline stated Evidence as of
Limitation: Projected payback and annualized revenue are not realized recovery.
Source: 2025, A Retrospective
Assessment: UnresolvedSaid
Ed Zitron Observation
“an analysis of reported cash burn and investments in OpenAI that proved the company burned more than $4 billion more than we know.”
Our reading: OpenAI burned more than $4 billion more cash than had been publicly known.
No horizon Evidence as of
Limitation: The historical period remains incompletely specified.
Source: 2025, A Retrospective
Assessment: UnresolvedSaid
Ed Zitron Observation
“there are no profitable generative AI companies”
Our reading: No generative AI company is profitable.
No horizon Evidence as of
Limitation: The latest-quarter interpretation is added by the evaluator.
Source: 2025, A Retrospective
Assessment: SupportedSaid
Gary Marcus Observation
“Why LLMs just can’t stop hallucinating.”
Our reading: LLMs are unable to stop hallucinating.
No horizon Evidence as of
Limitation: The Vectara figures come from one benchmark of summarization faithfulness. They may not reflect other tasks or other ways of measuring factual accuracy.
Source: AI Cartoon of The Year — and five (re)readings for 2026
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Neurosymbolic AI will displace pure LLMs over time; here’s why.”
Our reading: Neurosymbolic AI will eventually displace pure LLMs.
No deadline stated Evidence as of
Limitation: Neither 'neurosymbolic' nor 'pure LLM' is defined. Whether LLMs that use tools or agent harnesses count as neurosymbolic is disputed, and the outcome may depend on that choice.
Source: AI Cartoon of The Year — and five (re)readings for 2026
Assessment: UnresolvedSaid
Gary Marcus Forecast
“And LLMs will continue to exist.”
Our reading: LLMs will continue to exist even after the AI bubble falls apart.
No deadline stated Evidence as of
Limitation: The packet does not establish when or whether the anticipated broad collapse occurred.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“And without reliability, profits are limited.”
Our reading: Without reliability, profits from AI are limited.
No horizon Evidence as of
Limitation: The usable evidence is a reputable news report because the located primary documents could not be read with the required tool.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“But the economics don’t make sense, and never will, in large part because of the core technical problems that I have stressed repeatedly here and elsewhere since 2019.”
Our reading: The economics of LLMs will never make sense.
No deadline stated Evidence as of
Limitation: The detailed financial evidence concerns one major developer, not every LLM business.